Monocular depth estimation
[1]} has seen significant improvements [2]}, [3]} since the first adoption of deep learning by Eigen et al. [4]}. To further improve the performance, researchers explored many strategies such as multi-task learning with normal estimation [5]} and semantic segmentation [6]}, [7]} along with depth estimation, incorporating CRF [8]}, [9]}, integrating attention modules [10]}, [11]}, utilizing planar constraints [12]}, [10]}, conducting unsupervised learning using constrainsts such as left-right consistency [14]}, [15]}, [16]}, as well as two-stage approaches where stereo constraints are leveraged [17]}, [18]}, [19]}.
